Object location based on the received signal strength (RSSI) of a signal transmitted from reference objects has applications in many different fields of endeavor. For example, objects having small transceiver affixed to them can be tracked throughout a warehouse. Also, user of wireless devices can determine their locations in an environment using the RSSI received from reference devices.
One prior art method for locating an object RSSI calculates the signal space distance from calibration points within a wireless location system. In one particular prior art method, the location can be determined by calculating a distance in signal space between the current sample point and calibration points.
As an example, an area has a total of four reference-devices. At a given point in time, a wireless device will scan the wireless channels to determine the RSSI received from each reference device. The result can be written as a set of n RSSI measurements. For example, if the set of the reference device RSSIs at a given location can be called the sample, S, then the set of RSSI measured from each reference point will give S: {AP1, AP2, AP3, AP4}. The sample set can then be compared to the RSSIs of various calibration points. Each of the calibration points will have their own set of received signal strengths measurements from each reference device. The comparison can be done by calculating the Euclidean distance from each calibration point to the sample using the measured signal strengths and choosing the calibration point closest (having the smallest separation) to the sample set. The sample can be estimated to be located near that calibration point. This algorithm is discussed in “RADAR: An in-building RF based user location and tracking system”, by Parumuir Bahl and Venkata N. Padmanabhan, and published in Preceding of INFOCOM, 2000.
This method has several drawbacks. First, it assumes that the receiver can receive signal from all transmitters at all the time, thus failing to handle the situation where only a subset of the transmitters can be heard by the receiver, which is the typical case in large scale network. Second, since it fails to filter calibration points, all calibration points must be checked against the sample. As the number of reference devices and calibration points increase, the computational load and power consumption increases as well. Also, it is possible that the location algorithm can be thrown off by certain calibration points if all calibration points are used. What is need is a method and system for location estimation on wireless local networks via adaptive clustering.